Direct aperture optimization for IMRT using Monte Carlo generated beamlets

A hybrid column generation and simulated annealing algorithm for direct aperture optimization

The purpose of this work was to develop a hybrid column generation (CG) and simulated annealing (SA) algorithm for direct aperture optimization (H-DAO) and to show its effectiveness in generating high quality treatment plans for intensity modulated radiation therapy (IMRT) and mixed photon-electron beam radiotherapy (MBRT). The H-DAO overcomes limitations of the CG-DAO with two features improving aperture selection (branch-feature) and enabling aperture shape changes during optimization (SA-feature). The H-DAO algorithm iteratively adds apertures to the plan. At each iteration, a branch is created for each field provided. First, each branch determines the most promising aperture of its assigned field and adds it to a copy of the current apertures. Afterwards, the apertures of each branch undergo an MU-weight optimization followed by an SA-based simultaneous shape and MU-weight optimization and a second MU-weight optimization. The next H-DAO iteration continues the branch with the lowest objective function value. IMRT and MBRT treatment plans for an academic, a brain and a head and neck case generated using the CG-DAO and H-DAO were compared. For every investigated case and both IMRT and MBRT, the H-DAO leads to a faster convergence of the objective function value with number of apertures compared to the CG-DAO. In particular, the H-DAO needs about half the apertures to reach the same objective function value as the CG-DAO. The average aperture areas are 27% smaller for H-DAO than for CG-DAO leading to a slightly larger discrepancy between optimized and final dose. However, a dosimetric benefit remains. The H-DAO was successfully developed and applied to IMRT and MBRT. The faster convergence with number of apertures of the H-DAO compared to the CG-DAO allows to select a better compromise between plan quality and number of apertures.

Comparison of Monaco treatment planning system algorithms and Monte Carlo simulation for small fields in anthropomorphic RANDO phantom: The esophagus case

Background

In this study, the dose distributions obtained by the algorithms used in Monaco treatment planning system (TPS) and Monte Carlo (MC) simulation were compared for small fields in the anthropomorphic RANDO phantom, and then, the results were analyzed using the gamma analysis method.

Materials and Methods

In the study, dose distributions obtained from the collapse cone algorithm, MC algorithm, and MC simulation were examined. The EGSnrc was utilized for MC simulation.

Results

In radiation fields smaller than 3 cm × 3 cm, the doses calculated by the CC algorithm are particularly high in the region of lung/soft-tissue interfaces. In the region of soft-tissue/vertebral interfaces, the doses calculated by the CC algorithm and the MC algorithm are compatible with the MC simulation. For each algorithm, the main reason for the non-overlapping dose curves in small fields compared to MC simulation is that the lateral electronic equilibrium loss is not taken into account by the algorithms.

Conclusion

The doses calculated by the algorithms used in TPS may differ, especially in environments where density changes are sharp. Even if the radiation dose from different angles is calculated similarly in the target area by the algorithms, the calculated doses in the tissues in each radiation field path may be different. Therefore, to increase the quality of radiotherapy and to protect critical organs more accurately, the accuracy of the algorithms in TPS should be checked before treatment, especially in multi-field treatments such as stereotactic body radiation therapy and intensity-modulated radiotherapy for tumors in the abdominal region.

Enhanced optimization of volumetric modulated arc therapy plans using Monte Carlo generated beamlets

PURPOSE

A treatment planning system (TPS) produces volumetric modulated arc therapy (VMAT) plans by applying an optimization process to an objective function, followed by an accurate calculation of the final, deliverable dose. However, during the optimization step, a rapid dose calculation algorithm is required, which reduces its accuracy and its representation of the objective function space. Monte Carlo (MC) routines, considered the gold standard in accuracy, are currently too slow for practical comprehensive VMAT optimization. Therefore, we propose a novel approach called enhanced optimization (EO), which employs the TPS VMAT plan as a starting point, and applies small perturbations to nudge the solution closer to a true objective minimum. The perturbations consist of beamlet dose matrices, calculated using MC routines on a distributed-computing framework.

METHODS

DICOM files for clinical VMAT plans files are exported from the TPS and used to generate input files for the EGSnrc MC toolkit. Beamlet doses are calculated using the MC routines, each corresponding to a single multileaf collimator leaf from a single control point traveling 0.5 cm in or out of the field. A typical VMAT plan requires 5000 to 10 000 beamlets, which may be calculated overnight. This results in a ternary-valued objective function, which may use the same clinical objectives as the original VMAT plan. A simple greedy search algorithm is applied to minimize this function and determine the optimal set of ternary variables. The resulting modified control point parameters are imported into the TPS to calculate the final, deliverable dose, and to compare the EO plan with the original. EO was evaluated retrospectively on seven VMAT plans (two adult brain, one pediatric brain, two head and neck, and two prostate). Additionally, the use of stricter objectives was investigated for two of the cases: the left cochlea planning organ at risk (OAR) volume objective for the pediatric brain case, and the rectum objective for a prostate case.

RESULTS

EO produced improved objective scores (by 6% to 60%) and dose-volume histograms (DVH) for the brain plans and the head and neck plans. For each of these plans, the target dose minimum and homogeneity were preserved, while one or more of the OAR DVH's was reduced. Although EO also reduced the objective scores for the prostate plans (by 46% and 79%), their absolute score and DVH improvements were not substantial. The stricter objective on the pediatric brain case resulted in lower dose to the OAR without compromising the target dose. However, the rectum dose in the prostate case could not be improved without reducing dose homogeneity to the planning target volume, suggesting that VMAT prostate cases may already be highly optimized by the TPS.

CONCLUSION

We have developed a novel approach to improving the dose distribution of VMAT plans, which relies on MC calculations to provide small modifications to the control points. This method may be particularly useful for complex treatments in which a certain OAR is of concern and it is difficult for the treatment planner to obtain an acceptable solution with the TPS. Further development will reduce the beamlet computation time and result in more sophisticated EO treatment planning methods.

Dosimetric and Monte Carlo verification of jaws-only IMRT plans calculated by the Collapsed Cone Convolution algorithm for head and neck cancers

AIM

The aim of this study is to verify the Prowess Panther jaws-only intensity modulated radiation therapy (JO-IMRT) treatment planning (TP) by comparing the TP dose distributions for head-and-neck (H&N) cancer with the ones simulated by Monte Carlo (MC).

BACKGROUND

To date, dose distributions planned using JO-IMRT for H&N patients were found superior to the corresponding three-dimensional conformal radiotherapy (3D-CRT) plans. Dosimetry of the JO-IMRT plans were also experimentally verified using an ionization chamber, MapCHECK 2, and Octavius 4D and good agreements were shown.

MATERIALS AND METHODS

Dose distributions of 15 JO-IMRT plans of nasopharyngeal patients were recalculated using the EGSnrc Monte Carlo code. The clinical photon beams were simulated using the BEAMnrc. The absorbed dose to patients treated by fixed-field IMRT was computed using the DOSXYZnrc. The simulated dose distributions were then compared with the ones calculated by the Collapsed Cone Convolution (CCC) algorithm on the TPS, using the relative dose error comparison and the gamma index using global methods implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm, 1%/1 mm criteria.

RESULTS

There is a good agreement between the MC and TPS dose. The average gamma passing rates were 93.3 ± 3.1%, 92.8 ± 3.2%, 92.4 ± 3.4% based on the 3%/3 mm, 2%/2 mm, 1%/1 mm criteria, respectively.

CONCLUSIONS

According to the results, it is concluded that the CCC algorithm was adequate for most of the IMRT H&N cases where the target was not immediately adjacent to the critical structures.

Deterministic direct aperture optimization using multiphase piecewise constant segmentation

PURPOSE

Direct aperture optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post-processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford-Shah formulation.

METHODS

The Mumford-Shah based direct aperture optimization problem was formulated to include an L2-norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford-Shah function to partition the fluence into pairwise discrete segments. A proximal-class, first-order primal-dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity-one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs-were selected to test the new DAO method. For each patient, 20 non-coplanar beams were first selected using column generation, followed by the Mumford-Shah based DAO (DAO_MS ). For comparison, a popular and successful approach to DAO known as simulated annealing-a stochastic approach-was replicated. The simulated annealing DAO (DAO_SA ) plans were then created using the same beam angles and maximum number of segments per beam. PTV coverage, PTV homogeneity D95D5, and OAR sparing were assessed for each plan. In addition, high dose spillage, defined as the 50% isodose volume divided by the tumor volume, as well as conformity, defined as the van't Riet conformation number, were evaluated.

RESULTS

DAO_MS achieved essentially the same OAR doses compared with the DAO_SA plans for the GBM case. The average difference of OAR D_max and D_mean between the two plans were within 0.05% of the plan prescription dose. The lung case showed slightly improved critical structure sparing using the DAO_MS approach, where the average OAR D_max and D_mean were reduced by 3.67% and 1.08%, respectively, of the prescription dose. The DAO_MS plan substantially improved OAR dose sparing for the H&N patient, where the average OAR D_max and D_mean were reduced by over 10% of the prescription dose. The DAO_MS and DAO_SA plans were comparable for the GBM and LNG PTV coverage, while the DAO_MS plan substantially improved the H&N PTV coverage, increasing D99 by 6.98% of the prescription dose. For the GBM and LNG patients, the DAO_MS and DAO_SA plans had comparable high dose spillage but slightly worse conformity with the DAO_MS approach. For the H&N plan, DAO_MS was considerably superior in high dose spillage and conformity to the DAO_SA . The deterministic approach is able to solve the DAO problem substantially faster than the simulated annealing approach, with a 9.5- to 40-fold decrease in total solve time, depending on the patient case.

CONCLUSIONS

A novel deterministic direct aperture optimization formulation was developed and evaluated. It combines fluence map optimization and the multiphase piecewise constant Mumford-Shah segmentation into a unified framework, and the resulting optimization problem can be solved efficiently. Compared to the widely and commercially used simulated annealing DAO approach, it showed comparable dosimetry behavior for simple plans, and substantially improved OAR sparing, PTV coverage, PTV homogeneity, high dose spillage, and conformity for the more complex head and neck plan.

Total-variation regularization based inverse planning for intensity modulated arc therapy

Intensity modulated arc therapy (IMAT) delivers conformal dose distributions through continuous gantry rotation with constant or variable speed while modulating the field aperture shape and weight. The enlarged angular space and machine delivery constraints make inverse planning of IMAT more intractable as compared to its counterpart of fixed gantry IMRT. Currently, IMAT inverse planning is being done using two extreme methods: the first one computes in beamlet domain with a subsequent arc leaf sequencing, and the second proceeds in machine parameter domain with entire emphasis placed on a pre-determined delivery method without exploring potentially better alternative delivery schemes. Towards truly optimizing the IMAT treatment on a patient specific basis, in this work we propose a total-variation based inverse planning framework for IMAT, which takes advantage of the useful features of the above two existing approaches while avoiding their shortcomings. A quadratic optimization algorithm has been implemented to demonstrate the performance and advantage of the proposed approach. Applications of the technique to a prostate case and a head and neck case indicate that the algorithm is capable of generating IMAT plans with patient specific numbers of arcs efficiently. Superior dose distributions and delivery time are achieved with a maximum number of apertures of three for each field. As compared to conventional beamlet-based algorithms, our method regularizes the field modulation complexity during optimization, and permits us to obtain the best possible plan with a pre-set modulation complexity of fluences. As illustrated in both prostate and head-and-neck case studies, the proposed method produces more favorable dose distributions than the segment-based algorithms, by optimally accommodating the clinical need of intensity modulation levels for each individual field. On a more fundamental level, our formulation preserves the convexity of optimization and makes the search of the global optimal solution possible with a deterministic method.

MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data maps

PURPOSE

The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice.

METHODS

The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called "biophysical" map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process.

RESULTS

Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT + MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using volumetric modulated arc therapy. In the three cases, the required target prescription doses and constraints on organs at risk were fulfilled in a short enough time to allow routine clinical implementation. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements.

CONCLUSIONS

A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation time.

Improving IMRT delivery efficiency with reweighted L1-minimization for inverse planning

PURPOSE

This study presents an improved technique to further simplify the fluence-map in intensity modulated radiation therapy (IMRT) inverse planning, thereby reducing plan complexity and improving delivery efficiency, while maintaining the plan quality.

METHODS

First-order total-variation (TV) minimization (min.) based on L1-norm has been proposed to reduce the complexity of fluence-map in IMRT by generating sparse fluence-map variations. However, with stronger dose sparing to the critical structures, the inevitable increase in the fluence-map complexity can lead to inefficient dose delivery. Theoretically, L0-min. is the ideal solution for the sparse signal recovery problem, yet practically intractable due to its nonconvexity of the objective function. As an alternative, the authors use the iteratively reweighted L1-min. technique to incorporate the benefits of the L0-norm into the tractability of L1-min. The weight multiplied to each element is inversely related to the magnitude of the corresponding element, which is iteratively updated by the reweighting process. The proposed penalizing process combined with TV min. further improves sparsity in the fluence-map variations, hence ultimately enhancing the delivery efficiency. To validate the proposed method, this work compares three treatment plans obtained from quadratic min. (generally used in clinic IMRT), conventional TV min., and our proposed reweighted TV min. techniques, implemented by a large-scale L1-solver (template for first-order conic solver), for five patient clinical data. Criteria such as conformation number (CN), modulation index (MI), and estimated treatment time are employed to assess the relationship between the plan quality and delivery efficiency.

RESULTS

The proposed method yields simpler fluence-maps than the quadratic and conventional TV based techniques. To attain a given CN and dose sparing to the critical organs for 5 clinical cases, the proposed method reduces the number of segments by 10-15 and 30-35, relative to TV min. and quadratic min. based plans, while MIs decreases by about 20%-30% and 40%-60% over the plans by two existing techniques, respectively. With such conditions, the total treatment time of the plans obtained from our proposed method can be reduced by 12-30 s and 30-80 s mainly due to greatly shorter multileaf collimator (MLC) traveling time in IMRT step-and-shoot delivery.

CONCLUSIONS

The reweighted L1-minimization technique provides a promising solution to simplify the fluence-map variations in IMRT inverse planning. It improves the delivery efficiency by reducing the entire segments and treatment time, while maintaining the plan quality in terms of target conformity and critical structure sparing.

Evaluation of dose prediction error and optimization convergence error in four-dimensional inverse planning of robotic stereotactic lung radiotherapy

Inverse optimization of robotic stereotactic lung radiotherapy is typically performed using relatively simple dose calculation algorithm on a single instance of breathing geometry. Variations of patient geometry and tissue density during respiration could reduce the dose accuracy of these 3D optimized plans. To quantify the potential benefits of direct four‐dimensional (4D) optimization in robotic lung radiosurgery, 4D optimizations using 1) ray‐tracing algorithm with equivalent path‐length heterogeneity correction (4EPLopt), and 2) Monte Carlo (MC) algorithm (4MCopt), were performed in 25 patients. The 4EPLopt plans were recalculated using MC algorithm (4MCrecal) to quantify the dose prediction errors (DPEs). Optimization convergence errors (OCEs) were evaluated by comparing the 4MCrecal and 4MCopt dose results. The results were analyzed by dose‐volume histogram indices for selected organs. Statistical equivalence tests were performed to determine the clinical significance of the DPEs and OCEs, compared with a 3% tolerance. Statistical equivalence tests indicated that the DPE and the OCE are significant predominately in GTV D98%. The DPEs in V20 of lung, and D2% of cord, trachea, and esophagus are within 1.2%, while the OCEs are within 10.4% in lung V20 and within 3.5% in trachea D2%. The marked DPE and OCE suggest that 4D MC optimization is important to improve the dosimetric accuracy in robotic‐based stereotactic body radiotherapy, despite the longer computation time.

PACS numbers: 87.53.Ly, 87.55.km

Relationship of segment area and monitor unit efficiency in aperture-based IMRT optimization

In step‐and‐shoot IMRT plans, aperture‐based optimization (or one‐step optimization) has been considered as a means of improving monitor unit (MU) efficiency compared to fluence‐based optimization (or two‐step optimization). However, the extent of improvement on MU efficiency varies, depending on the implementation and design of one‐step optimization. In this paper, we attempted to investigate MU efficiency issue in two methods of one‐step optimization implemented in two commercial treatment planning systems (TPSs). Five patients with nasopharyngeal cancer and five patients with advanced prostate cancer were selected for this study. For these patients, clinically used IMRT plans were generated using the Direct Machine Parameter Optimization (DMPO) in the Pinnacle TPS. New IMRT plans were created using the Direct Aperture Optimization (DAO) method in the Panther TPS. For the purpose of this study, we used the similar planning dose objectives and beam configurations with a similar total number of segments in each pair of DMPO and DAO plans. With similar plan quality, DMPO plans required more MUs than DAO plans. The average number of MUs (expressed in mean ±1 SD) for the DMPO and DAO plans was 1,169±186 and 671±135 for the nasopharynx cases, and 711±48 and 400±65 for the prostate cases, respectively. The average segment areas (expressed in mean ±1 SD) for the DMPO plans were smaller than those for the DAO plans: 46.0±7.6 cm2 vs. 100.9±32.3 cm2 for the nasopharynx cases, and 58.3±17.2 cm2 vs. 97.4±35.0 cm2 for the prostate cases, respectively. In conclusion, two one‐step optimization algorithms, DMPO and DAO, resulted in much different MU efficiency with the similar number of segments and optimization parameters. This MU difference is largely attributed to the fact that large area segments are used more often in DAO plans than in DMPO plans.

PACS number: 87.55.de

Dose optimization with first-order total-variation minimization for dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT)

PURPOSE

A new treatment scheme coined as dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) has recently been proposed to bridge the gap between IMRT and VMAT. By increasing the angular sampling of radiation beams while eliminating dispensable segments of the incident fields, DASSIM-RT is capable of providing improved conformity in dose distributions while maintaining high delivery efficiency. The fact that DASSIM-RT utilizes a large number of incident beams represents a major computational challenge for the clinical applications of this powerful treatment scheme. The purpose of this work is to provide a practical solution to the DASSIM-RT inverse planning problem.

METHODS

The inverse planning problem is formulated as a fluence-map optimization problem with total-variation (TV) minimization. A newly released L1-solver, template for first-order conic solver (TFOCS), was adopted in this work. TFOCS achieves faster convergence with less memory usage as compared with conventional quadratic programming (QP) for the TV form through the effective use of conic forms, dual-variable updates, and optimal first-order approaches. As such, it is tailored to specifically address the computational challenges of large-scale optimization in DASSIM-RT inverse planning. Two clinical cases (a prostate and a head and neck case) are used to evaluate the effectiveness and efficiency of the proposed planning technique. DASSIM-RT plans with 15 and 30 beams are compared with conventional IMRT plans with 7 beams in terms of plan quality and delivery efficiency, which are quantified by conformation number (CN), the total number of segments and modulation index, respectively. For optimization efficiency, the QP-based approach was compared with the proposed algorithm for the DASSIM-RT plans with 15 beams for both cases.

RESULTS

Plan quality improves with an increasing number of incident beams, while the total number of segments is maintained to be about the same in both cases. For the prostate patient, the conformation number to the target was 0.7509, 0.7565, and 0.7611 with 80 segments for IMRT with 7 beams, and DASSIM-RT with 15 and 30 beams, respectively. For the head and neck (HN) patient with a complicated target shape, conformation numbers of the three treatment plans were 0.7554, 0.7758, and 0.7819 with 75 segments for all beam configurations. With respect to the dose sparing to the critical structures, the organs such as the femoral heads in the prostate case and the brainstem and spinal cord in the HN case were better protected with DASSIM-RT. For both cases, the delivery efficiency has been greatly improved as the beam angular sampling increases with the similar or better conformal dose distribution. Compared with conventional quadratic programming approaches, first-order TFOCS-based optimization achieves far faster convergence and smaller memory requirements in DASSIM-RT.

CONCLUSIONS

The new optimization algorithm TFOCS provides a practical and timely solution to the DASSIM-RT or other inverse planning problem requiring large memory space. The new treatment scheme is shown to outperform conventional IMRT in terms of dose conformity to both the targetand the critical structures, while maintaining high delivery efficiency.

Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization

PURPOSE

Improving dose calculation accuracy is crucial in intensity-modulated radiation therapy (IMRT). We have developed a method for generating a phase-space-based dose kernel for IMRT planning of lung cancer patients.

METHODS

Particle transport in the linear accelerator treatment head of a 21EX, 6 MV photon beam (Varian Medical Systems, Palo Alto, CA) was simulated using the EGSnrc/BEAMnrc code system. The phase space information was recorded under the secondary jaws. Each particle in the phase space file was associated with a beamlet whose index was calculated and saved in the particle's LATCH variable. The DOSXYZnrc code was modified to accumulate the energy deposited by each particle based on its beamlet index. Furthermore, the central axis of each beamlet was calculated from the orientation of all the particles in this beamlet. A cylinder was then defined around the central axis so that only the energy deposited within the cylinder was counted. A look-up table was established for each cylinder during the tallying process. The efficiency and accuracy of the cylindrical beamlet energy deposition approach was evaluated using a treatment plan developed on a simulated lung phantom.

RESULTS

Profile and percentage depth doses computed in a water phantom for an open, square field size were within 1.5% of measurements. Dose optimized with the cylindrical dose kernel was found to be within 0.6% of that computed with the nontruncated 3D kernel. The cylindrical truncation reduced optimization time by approximately 80%.

CONCLUSIONS

A method for generating a phase-space-based dose kernel, using a truncated cylinder for scoring dose, in beamlet-based optimization of lung treatment planning was developed and found to be in good agreement with the standard, nontruncated scoring approach. Compared to previous techniques, our method significantly reduces computational time and memory requirements, which may be useful for Monte-Carlo-based 4D IMRT or IMAT treatment planning.

Motion management with phase-adapted 4D-optimization

Cancer treatment with ionizing radiation is often compromised by organ motion, in particular for lung cases. Motion uncertainties can significantly degrade an otherwise optimized treatment plan. We present a spatiotemporal optimization method, which takes into account all phases of breathing via the corresponding 4D-CTs and provides a 4D-optimal plan that can be delivered throughout all breathing phases. Monte Carlo dose calculations are employed to warrant for highest dosimetric accuracy, as pertinent to study motion effects in lung. We demonstrate the performance of this optimization method with clinical lung cancer cases and compare the outcomes to conventional gating techniques. We report significant improvements in target coverage and in healthy tissue sparing at a comparable computational expense. Furthermore, we show that the phase-adapted 4D-optimized plans are robust against irregular breathing, as opposed to gating. This technique has the potential to yield a higher delivery efficiency and a decisively shorter delivery time.

Search for IMRT inverse plans with piecewise constant fluence maps using compressed sensing techniques

An intensity-modulated radiation therapy (IMRT) field is composed of a series of segmented beams. It is practically important to reduce the number of segments while maintaining the conformality of the final dose distribution. In this article, the authors quantify the complexity of an IMRT fluence map by introducing the concept of sparsity of fluence maps and formulate the inverse planning problem into a framework of compressing sensing. In this approach, the treatment planning is modeled as a multiobjective optimization problem, with one objective on the dose performance and the other on the sparsity of the resultant fluence maps. A Pareto frontier is calculated, and the achieved dose distributions associated with the Pareto efficient points are evaluated using clinical acceptance criteria. The clinically acceptable dose distribution with the smallest number of segments is chosen as the final solution. The method is demonstrated in the application of fixed-gantry IMRT on a prostate patient. The result shows that the total number of segments is greatly reduced while a satisfactory dose distribution is still achieved. With the focus on the sparsity of the optimal solution, the proposed method is distinct from the existing beamlet- or segment-based optimization algorithms.

Major reduction of monitor units with the avoidance of leaf-sequencing step by direct aperture based IMRT optimisation

BACKGROUND AND PURPOSE

Compared with conventional 3D conformal radiotherapy (3D-CRT) the use of intensity-modulated radiation therapy (IMRT) has increased monitor units (MUs) in the delivery of prescribed dose to the patient and thus a potential risk of radiation-induced secondary cancer. Due to the elimination of the leaf-sequencing step in direct aperture based IMRT optimisation (DABO) the MUs in the beam delivery can be reduced. We compared MUs calculated by DABO with other IMRT techniques and 3D-CRT.

MATERIAL AND METHODS

Treatment plans for five head and neck cancer patients using dynamic IMRT technique (DMLC) and step-and-shoot (SMLC) technique (Varian Helios Cadplan), 3D-CRT (Varian Eclipse) and a home-made DABO were produced. The total number of MUs, dose coverage and standard deviation of prescribed dose in planning target volume (PTV) between different techniques were compared.

RESULTS

In all patients the PTV coverage and sparing of critical structures between the DABO, Helios DMLC and SMLC IMRT techniques was equivalent. Average MUs for beam delivery were 883 MU, 683 MU, 379 MU and 411 MU for DMLC, SMLC, DABO and 3D-CRT, respectively.

CONCLUSIONS

The DABO IMRT technique is able to produce treatment plans equivalent in target coverage, dose uniformity and normal tissue sparing compared with the commercial IMRT techniques. The number of MUs with DABO were reduced with a factor of 2.3 (DMLC) and 1.8 (SMLC). The study suggests that with DABO IMRT technique the risk of secondary cancer can be reduced without compromise in the quality of treatment plans.

Direct aperture optimization as a means of reducing the complexity of Intensity Modulated Radiation Therapy plans

Intensity Modulated Radiation Therapy (IMRT) is a means of delivering radiation therapy where the intensity of the beam is varied within the treatment field. This is done by dividing a large beam into many small beamlets. Dose constraints are assigned to both the target and sensitive structures and computerised inverse optimization is performed to find the individual weights of this large number of beamlets. The computer adjusts the intensities of these beamlets according to the required planning dose objectives. The optimized intensity patterns are then decomposed into a series of deliverable multi leaf collimator (MLC) shapes in the sequencing step.

One of the main problems of IMRT, which becomes even more apparent as the complexity of the IMRT plan increases, is the dramatic increase in the number of Monitor Units (MU) required to deliver a fractionated treatment. The difficulty with this increase in MU is its association with increased treatment times and a greater leakage of radiation from the MLCs increasing the total body dose and the risk of secondary cancers in patients. Therefore one attempts to find ways of reducing these MU without compromising plan quality.

The design of inverse planning systems where the beam is divided into small beamlets to produce the required intensity map automatically introduces complexity into IMRT treatment planning. Plan complexity is associated with many negative factors such as dosimetric uncertainty and delivery issues A large search space is required necessitating much computing power. However, the limitations of the delivery technology are not taken into consideration when designing the ideal intensity map therefore a further step termed the sequencing step is required to convert the ideal intensity map into a deliverable one.

Many approaches have been taken to reduce the complexity. These include setting intensity limits, putting penalties on the cost function and using smoothing filters Direct Aperture optimization (DAO) incorporates the limitations of the delivery technology at the initial design of the intensity map thereby eliminating the sequencing step. It also gives control over the number of segments and hence control over the complexity of the plan although the design of the segments is independent of the person preparing the plan.

Using total-variation regularization for intensity modulated radiation therapy inverse planning with field-specific numbers of segments

Currently, there are two types of treatment planning algorithms for intensity modulated radiation therapy (IMRT). The beamlet-based algorithm generates beamlet intensity maps with high complexity, resulting in large numbers of segments in the delivery after a leaf-sequencing algorithm is applied. The segment-based direct aperture optimization (DAO) algorithm includes the physical constraints of the deliverable apertures in the calculation, and achieves a conformal dose distribution using a small number of segments. However, the number of segments is pre-fixed in most of the DAO approaches, and the typical random search scheme in the optimization is computationally intensive. A regularization-based algorithm is proposed to overcome the drawbacks of the DAO method. Instead of smoothing the beamlet intensity maps as in many existing methods, we include a total-variation term in the optimization objective function to reduce the number of signal levels of the beam intensity maps. An aperture rectification algorithm is then applied to generate a significantly reduced number of deliverable apertures. As compared to the DAO algorithm, our method has an efficient form of quadratic optimization, with an additional advantage of optimizing field-specific numbers of segments based on the modulation complexity. The proposed approach is evaluated using two clinical cases. Under the condition that the clinical acceptance criteria of the treatment plan are satisfied, for the prostate patient, the total number of segments for five fields is reduced from 61 using the Eclipse planning system to 35 using the proposed algorithm; for the head and neck patient, the total number of segments for seven fields is reduced from 107 to 28. The head and neck result is also compared to that using an equal number of four segments for each field. The comparison shows that using field-specific numbers of segments achieves a much improved dose distribution.

Comparison of direct machine parameter optimization versus fluence optimization with sequential sequencing in IMRT of hypopharyngeal carcinoma

Background

To evaluate the effects of direct machine parameter optimization in the treatment planning of intensity-modulated radiation therapy (IMRT) for hypopharyngeal cancer as compared to subsequent leaf sequencing in Oncentra Masterplan v1.5.

Methods

For 10 hypopharyngeal cancer patients IMRT plans were generated in Oncentra Masterplan v1.5 (Nucletron BV, Veenendal, the Netherlands) for a Siemens Primus linear accelerator.

For optimization the dose volume objectives (DVO) for the planning target volume (PTV) were set to 53 Gy minimum dose and 59 Gy maximum dose, in order to reach a dose of 56 Gy to the average of the PTV. For the parotids a median dose of 22 Gy was allowed and for the spinal cord a maximum dose of 35 Gy. The maximum DVO to the external contour of the patient was set to 59 Gy. The treatment plans were optimized with the direct machine parameter optimization ("Direct Step & Shoot", DSS, Raysearch Laboratories, Sweden) newly implemented in Masterplan v1.5 and the fluence modulation technique ("Intensity Modulation", IM) which was available in previous versions of Masterplan already. The two techniques were compared with regard to compliance to the DVO, plan quality, and number of monitor units (MU) required per fraction dose.

Results

The plans optimized with the DSS technique met the DVO for the PTV significantly better than the plans optimized with IM (p = 0.007 for the min DVO and p < 0.0005 for the max DVO). No significant difference could be observed for compliance to the DVO for the organs at risk (OAR) (p > 0.05). Plan quality, target coverage and dose homogeneity inside the PTV were superior for the plans optimized with DSS for similar dose to the spinal cord and lower dose to the normal tissue. The mean dose to the parotids was lower for the plans optimized with IM. Treatment plan efficiency was higher for the DSS plans with (901 ± 160) MU compared to (1151 ± 157) MU for IM (p-value < 0.05).

Renormalization of the IM plans to the mean of the dose to 95% of the PTV (D₉₅) of the DSS plans, resulted in similar target coverage and dose to the parotids for both strategies, at the cost of a significantly higher dose to the normal tissue and maximum dose to the target. The relative volume of the PTV receiving 107% or more of the prescription dose V₁₀₇ increased to 35.5% ± 20.0% for the IM plan as compared to a mean of 0.9% ± 0.9% for the DSS plan.

Conclusion

The direct machine parameter optimization is a major improvement compared to the fluence modulation with subsequent leaf sequencing in Oncentra Masterplan v1.5. The resulting dose distribution complies better with the DVO and better plan quality is achieved for identical specification of DVO. An additional asset is the reduced number of MU as compared to IM.

Performance of a hybrid MC dose algorithm for IMRT optimization dose evaluation

This paper presents a hybrid intensity modulated radiation therapy (IMRT) optimization strategy which combines the speed of pencil beam (PB) and the accuracy of Monte Carlo (MC) dose calculations. After an initial deliverable-based optimization using a PB algorithm, doses are recomputed using the VMC++ MC code to determine dose correction factors, which are then utilized during further PB-based optimization. The hybrid method is benchmarked with respect to full MC deliverable-based optimization for ten prostate and ten head-and-neck IMRT plans. Final optimized plans are compared in terms of dose-volume indices used for the plan optimization. Dose prediction errors (DPEs) and optimization convergence errors (OCEs) at intermediate steps of the hybrid sequence are evaluated. The hybrid method is found to produce optimized plans that are clinically equivalent to full MC-based optimization, yet requires only 40% of the number of MC dose calculations. With the hybrid strategy presented here, MC-based optimization results are achieved in 35 min or less on a modest computing cluster. While the initial PB-deliverable-based optimization is found to have DPEs and OCEs of up to 3 Gy relative to the 65-73 Gy prescription doses, application of the first MC correction reduces the average DPEs to less than 0.3 Gy for the prostate plans and less than 0.06 Gy for the head and neck plans. The maximum observed DPE or OCE is 0.7 Gy after 1 MC dose correction, indicating that a single MC dose calculation correction might be sufficient for IMRT optimization.